Feedback Mechanism Based On Load Balancing Improve Algorithm Research

"Load Balancing" technology is an advanced technology, can effectively solve the problem of uneven distribution of network load. Through the appropriate load balancing algorithm, a asonable allocation of the task request to the all servers of the network, while large amounts of data traffic allocated to more than one network link to the shared, make full use of network resources, increase network availability, scalability and flexibility and so on.There are various types of load balancing. A good load balancing strategy must have the appropriate load balancing algorithm, but also an effective means of detecting the state of network load. At present, a single load balancing algorithm difficult to meet the requirements, the need to combine a variety of algorithms used compositely. Load balancing in the cluster have important applications, especially for large sites to use the Web cluster is of great significance.Feedback mechanism based on load balancing algorithm, we must consider the sampling period. Ideally, the sampling interval, the shorter the more satisfactory results. But the too frequent sampling also will increase the burden on the system. Through the forecasting theory and forecasting methods for learning and research, able to recognize, a pre-load prediction strategy can be use to estimate the server load, and intelligence to predict the next period of network status, and then use the appropriate strategies to allocate task. It can reduce the sampling rate appropriately to ease the system pressure.This article is based on randomized dynamic algorithm, combined with the load forecast and add correction factor, to design a load balancing improve algorithm. The algorithm improves the performance of load balancing, and extend the sampling period to ease the pressure of the system. The algorithm composite considers current and future state of load; hosts on the network are different, the effect is particularly significant. Comprehensive consideration and the difference is the central idea. Additionally, the algorithm is suitable for a wide range, the flexibility to stretch and shrink according to the actual needs.